THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30.
Instructions for updating:
Use tf.losses.sparse_softmax_cross_entropy instead. Note that the order of the logits and labels arguments has been changed.

weights acts as a coefficient for the loss. If a scalar is provided,
then the loss is simply scaled by the given value. If weights is a
tensor of size [batch_size], then the loss weights apply to each
corresponding sample.

Args:

logits: [batch_size, num_classes] logits outputs of the network .

labels: [batch_size, 1] or [batch_size] labels of dtype int32 or int64
in the range [0, num_classes).

weights: Coefficients for the loss. The tensor must be a scalar or a tensor
of shape [batch_size] or [batch_size, 1].

scope: the scope for the operations performed in computing the loss.

Returns:

A scalar Tensor representing the mean loss value.

Raises:

ValueError: If the shapes of logits, labels, and weights are
incompatible, or if weights is None.